It is desired to extract textual information from images captured by various capture devices.
The information extraction process (also referred to as recognition process) is problematic and error prone.
Various recognition error correction processes (also referred to as verification processes) were developed in order to automatically correct errors of the recognition process.
Some of these verification processes are based upon fuzzy search engines that search a dictionary or lexicon for the best matching key. The best matching key is the key that is closest to the recognition process result.
The fuzzy search error rate depends upon the density, or level of population of the dictionary or the lexicon. The higher this density becomes the probability of error increases.
In order to reduce the effect of this density on the fuzzy search error rate some recognition error correction processes alter their matching algorithm such as to ignore parts of the dictionary (or lexicon) while searching other parts of the dictionary (or lexicon). These modifications prevent the same matching algorithms from being used in multiple applications, which can slow down and complicate the matching process and are also error prone.
There is a growing need to correct errors of a recognition process in an efficient manner.